Case Study

Fintech Dashboard

A comprehensive wealth management platform providing real-time portfolio analytics, compliance monitoring, and AI-powered investment recommendations.

The Challenge

Understanding the Problem

Wealth managers needed a unified dashboard to monitor client portfolios across multiple custodians, ensure regulatory compliance, and provide personalized investment recommendations.

Our Approach

We built a real-time data aggregation layer connecting to multiple financial data providers, with ML-powered recommendation engine and automated compliance checking.

Phase 1

Robo-Advisor Module

Phase 2

Tax-Loss Harvesting Automation

Phase 3

ESG Scoring Integration

Phase 4

Natural Language Portfolio Queries

The Solution

Unified dashboard aggregating data from 20+ custodians with real-time NAV calculations, risk analytics, and AI-generated portfolio rebalancing suggestions.

-80% Portfolio Review
20+ Data Sources
99.9% Compliance

The Outcome

Reduced portfolio review time by 80%. Improved compliance accuracy to 99.9%. Generated $50M in additional AUM through personalized recommendations.

Impact: $50M Additional AUM

Technical Deep Dive

Engineering Excellence

A comprehensive look at the technical architecture and implementation details that power this solution.

architecture

Event-driven architecture with Apache Flink for real-time stream processing. Time-series database for market data with sub-second query performance.

security

SOC 2 Type II and SEC compliant. Multi-factor authentication with hardware key support. End-to-end encryption for all client data.

System Architecture

Market Feeds

Bloomberg, Plaid

Apache Flink

Stream Processing

TimescaleDB

Time-series Data

ML Engine

Recommendations

Dashboard

Next.js Frontend

Stream: feeds → flink

Store: flink → tsdb

Analyze: tsdb → ml

Display: ml → ui

Development Journey

From Concept to Launch

4 weeks

Data Architecture

Designed real-time data aggregation from 20+ custodians and market data providers.

10 weeks

Core Platform

Built portfolio analytics engine, NAV calculations, and risk metrics dashboards.

6 weeks

AI Recommendations

Developed ML models for portfolio optimization and personalized investment suggestions.

4 weeks

Compliance Module

Implemented automated regulatory checks, audit trails, and reporting systems.

4 weeks

Security & Launch

Completed SOC 2 audit, penetration testing, and phased rollout to advisors.

Measurable Impact

Key Results

Primary Outcome

$50M Additional AUM

Direct business value delivered.

-80%

Portfolio Review

Time reduction

20+

Data Sources

Unified custodians

99.9%

Compliance

Accuracy rate

Impact Analysis

Portfolio Review

Before

4 hours

After

45 minutes

80% Faster

Compliance Errors

Before

12/month

After

0/month

100% Accurate

Client Reporting

Before

Weekly

After

Real-time

Instant Access

Technology Stack

Tools & Frameworks

Next.jsPythonApache FlinkTimescaleDBRedisKubernetesPlaidBloomberg API

Implementation

Real-time Portfolio NAV

Stream processing for real-time NAV calculation as market prices update.

logic.js

python

1class NAVCalculator:
2    def calculate_nav(self, holdings, prices):
3        nav = sum(
4            Decimal(h['quantity']) * Decimal(prices.get(h['symbol'], 0))
5            for h in holdings
6        )
7        return {'nav': float(nav), 'timestamp': datetime.utcnow()}

Performance

Performance Audits

Performance

SEO

Accessibility

100

Best Practices

"This platform transformed how we serve clients. What took hours now takes minutes."

Managing Partner, Wealth Advisory Firm

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